An algorithm for conducting UAVs' dependability & self-recovery system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the purpose of improving weapon effectiveness evaluation, this research uses system dynamic as the model. The simulation scenario was developed by combining the reconnaissance aircrafts and attack fighters in terms of joint operations, establishing subsystems of dependability, self-recovery, and reconnaissance. In addition, the swarm Unmanned Aerial Vehicles (UAV) system effectiveness assessment model was integrated with those subsystems to simulate the UAV flight mission in order to identify the impacts and correlations within the mission execution process and to analyze the swarm UAV system combat effectiveness. Simulation results have found: Firstly, by using the System Dynamic to set up swarm UAV combat effectiveness has made interactive impacts on evaluation model. Secondly, the interrelation of each effectiveness indicator change within the operational phases can be analyzed. Thirdly, the swarm intelligence has made a positive impact on UAV effectiveness outputs in which the problems of less UAV can be resolved by the self-recovery while the numbers of UAV is reduced. Finally, the joint operation can depend on various weapon characteristics to improve combat effectiveness. However, the joint operation is complex which requires systematic aspects to analyze the interaction of every effectiveness indicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it